In this paper we present a system which learns to recognize objects through interaction by exploiting the principle of sensorimotor coordination. The system uses a learning architecture which is composed of reactive and deliberative layers. The reactive layer consists of a database of behaviors that are modulated to produce a desired behavior. In this work we have implemented and installed in our architecture an object manipulation behavior inspired by the concept that infants learn about their environment through manipulation. While manipulating objects, both proprioceptive data and exteroceptive data are recorded. Both of these types of data are combined and statistically analyzed in order to extract important parameters that distinctively describe the object being manipulated. This data is then clustered using the standard k-means algorithm and the resulting clusters are labeled. The labeling is used to train a radial basis function network for classifying the clusters. The performance of the system has been tested on a kinematically complex walking robot capable of manipulating objects with two legs used as arms, and it has been found that the trained neural network is able to classify objects even when only partial sensory data is available to the system. Our preliminary results demonstrate that this method can be effectively used in a robotic system which learns from experience about its environment.